WEB MINING - THE ONTOLOGY APPROACH
Ee-Peng Lim1) and Aixin Sun2)
1) School of Computer Engineering
Nanyang Technological University, Singapore
Email: aseplim@ntu.edu.sg
2) School of Computer Science and Engineering
University of New South Wales, Sydney, Australia
Email: aixinsun@cse.unsw.edu.au
ABSTRACTThe World Wide Web today provides users access to extremely large number of Web sites many of whichcontain information of education and commercial values. Due to the unstructured and semi-structured natureof Web pages and the design idiosyncrasy of Web sites, it is a challenging task to develop digital libraries fororganizing and managing digital content from the Web. Web mining research, in its last 10 years, has on theother hand made significant progress in categorizing and extracting content from the Web. In this paper, we
represent ontology as a set of concepts and their inter-relationships relevant to some knowledge domain. The
knowledge provided by ontology is extremely useful in defining the structure and scope for mining Web
content. We will therefore review Web mining and describe the ontology approach to Web mining. The
application of these Web mining techniques to digital library systems will also be discussed.

INTRODUCTION
Motivation
The ubiquity of Web can be characterized by the enormous volume and coverage of Web content, thephenomenal number of Web users and businesses, the vast number of computers and devices accessing Web,and the large number of Web-based applications. A survey conducted by OCLC in 2002 revealed that there
were 3 million public Websites and 1.4 billion Web pages at that point in time[1]. A ubiquitous Web has
certainly led to some fundamental changes to the design of digital libraries. Among them is the searchbehavior of digital library users. Users today perform more searches using Web search engines than OPAC
systems. Increasingly, Web is the preferred or de facto source of information. A May 2004 survey by
Nielsen reported that an average surfer went online 30 times for more than 24 hours in total during a month1.
The ubiquity of Web offers some obvious explanations, namely:• The coverage of Web content is so large that it is difficult for any traditional digital libraries to match;• The ability of to browse Web content directly on the users’ computers and the ease of downloading themis clearly a big draw; and• The availability of Web search engine (e.g., Google2) and Web directories (e.g., Yahoo!3, DMOZ4) has
helped tremendously simplified the process of searching Web content.
Nevertheless, Web content is not always easy to use. Due to the unstructured and semi-structured nature of
Web pages and the design idiosyncrasy of Web sites, it is a challenging task to develop digital libraries fororganizing and managing digital content from the Web. Berners-Lee et al. therefore introduced the idea of1

http://www.caslon.com.au/index.htm
www.google.com
3
www.yahoo.com
4
www.dmoz.com
2

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Semantic Web which refers to the construction of a machine-understandable semantic layer over the existing
Web content so as to support better information processing and Web services [2]. While Semantic Web may
take several years to realise, digital library researchers are turning to Web mining techniques to improve theaccessibility of Web content[3]. The well established Web mining techniques include Web classification [4]and Web extraction [5,6].
Objectives
Web mining techniques have shown promising performance in research experiments. Their actual
deployment in live Web data, in contrast, has been fairly limited due to a lack of background semantics
required for processing the text data, links, and other elements in Web pages. In this respect, an ontology
which gives a conceptual description of the background semantics can serve as a very useful input to the Webmining problems [7]. An ontology refers to a set of concepts and the relationships, together known as
ontology entities, describing the information within an application domain. When an ontology is used insolving a Web classification or extraction problem, the results obtained can be associated with the ontology
entities making them easier to understand. This is a big advantage because each ontology often represents
knowledge agreed upon by users and applications of a domain. For example, within the University domain,{Professor, Student, Course} and {Teach, Register, Supervise} are the common concepts and relationships
respectively. University Web pages are likely to centered around these concepts and related concept
instances are likely to be linked in one way or another.
As the languages for defining ontologies and using the latter in marking up Web content become well
accepted [8], we see an increasing use of ontology in Web mining. In this paper, we will give an overview of
ontology-based Web mining. In ontology-based Web mining, we are often interested in discovering the
instances of concepts and relationships in a given ontology, or using them to discover other usefulknowledge. These Web mining techniques can potentially be deployed in a digital library system to enhance
the access to Web content. This paper will later present our research on homepage mining and homepage
relationship mining where homepages representing instances of concepts and pairs of homepages
representing instances of relationships are to be mined respectively [9,10].
Paper Outline
The rest of the paper is organised as follows. The definition of ontology will first be given. We will thenelaborate on ontology-based Web mining. Following that, our research in homepage mining and homepage
relationship mining will be described. Finally, we give a conclusion of the paper.

WHAT IS ONTOLOGY?
The term ontology can be defined in many different ways. Genersereth and Nilsson defined an ontology as
an explicit specification of a set of of objects, concepts, and other entities that are presumed to exist in somearea of interest and the relationships that hold them [11]. As implied by the above general definition, an
ontology is domain dependent and it is designed to be shared and reusable. Usually, ontologies are defined toconsist of abstract concepts and relationships (or properties) only. In some rare cases, ontologies are defined
to also include instances of concepts and relationships[12].
To solve a problem using ontology, a formal definition is required. For the purpose of this paper, we define
an ontology to be a set of concepts C and relationships R. The relationships in R can be either taxonomic or
non-taxonomic. For example, Figure 1 depicts a simple University ontology consisting of a set of concepts
Cuniv = {Person, Faculty, Staff, Student, Department, Project, Course}, and a set of relationships Runiv ={Department_Of(Person, Department), Member_Of(Person, Project), Instructor_Of(Course, Person),Superclass_Of(Faculty, Person), Superclass_Of(Staff, Person), Superclass_Of(Student, Person)}.
Superclass_Of represents the taxonomic relationship while the rest are not. With this definition, the instancesof an ontology refer to the instances of its concepts and relationships. If each concept instance exists in theform of a Web page, a relationship instance will then exist in the form of a Web page pair. This view has
been adopted in most the Web classification research. On the other hand, if each concept instance exists in2the form an HTML element, a relationship instance will then exist in the form of an HTML element pair.
This alternative view is usually adopted in Web extraction research. It is noted that other forms or hybrid
forms of concept instances may also exist for some Websites.
Department_Of
Member_Of

PersonDepartment

Concept

Project

Instructor_Of

CourseFaculty

Staff

Legend:Student

Non-Taxonomic
Relationship
Taxonomic
RelationshipFig. 1 An University Ontology Example

The construction of an ontology itself is an ongoing research topic. The construction process can be manualwith the help of some ontology editing tools, e.g. OntoEdit [13], or automatic using a collection of training
documents, e.g.OntoLearn[14]. There are also different languages for representing an ontology so as tosupport exchanges of ontology knowledge. Su and Ilebrekke gave a good description and comparison ofthese languages. In this paper, we assume that an ontology is given to conduct Web mining. Since
knowledge exchange is outside the scope of our discussion, we do not intend to use any ontology
representation language in this paper.
OVERVIEW OF ONTOLOGY-BASED WEB MINING
Overview of Web Mining
Web mining refers to the discovery of knowledge from Web data that include Web pages, media objects on
the Web, Web links, Web log data, and other data generated by the usage of Web data. Kosala and Blockeelclassified Web mining into: (a) Web content mining, (b) Web structure mining and (c) Web usage mining [3].
Web content mining refers to mining knowledge from Web pages and other Web objects. Web structuremining refers to mining knowledge about link structure connecting Web pages and other Web objects. Web
usage mining refers to the mining of usage patterns of Web pages found among users accessing a Website.
Among the three, Web content mining is perhaps studied most extensively due to the prior work in text
mining. The traditional topics covered by Web content mining include:•••

Web page classification: This involves the classification of Web pages under some pre-defined
categories that may be organised in a tree or other structures.
Web clustering: This involves the grouping of Web pages based on the similarities among them. Each
resultant group should have similar Web pages while Web pages from different resultant groups should
be dissimilar.
Web extraction: This involves extracting HTML elements, term phrases, or tuples from Web pages that
represent some required concept instances, e.g., person names, location names, book records, etc..

Web Mining and Ontologies
In all the above types of Web mining, ontologies can be applied in the following two general approaches:•

When both ontology and the instances of ontology entities are known: This usually applies to cases
where instances of ontology have been identified among the input Web data. With this additional data
semantics, Web mining techniques can discover knowledge that is more meaningful to the users. For
example, ontology-based Web clustering can use HTML elements corresponding to concept instances asfeatures to derive more accurate clusters. If Web pages are concept instances, ontology-based Web site
structure mining can derive linkage pattern among concepts from Web pages for Website design3improvements.
•

When only ontology are available as input semantic structures: Ontologies can also be used as
background semantic structures for Web mining. For example, instead of categorizing Web pages into
categories, ontology-based Web page classification may classify Web pages as concept instances andWeb page pairs as relationship instances. This allows Web pages to be searched using more expressive
search queries involving search conditions on concepts and/or relationships. In ontology-based Web
extraction, one may address the problem of extracting both HTML elements as concept instances andfinding related pairs of HTML elements.

Digital Library Applications of Ontology-based Web Mining
Ontology-based Web mining, like traditional Web mining, is useful to many different digital library
applications. We can group these applications under the following classes:•

Improved search to Web data: With additional ontological semantics, Web data can be indexed by theirconcepts and relationships to support expressive search queries. For example, using the University
ontology in Figure 1, we can query faculty member information working on digital library projects byassigning query term “digital library” to the project concept and specifying that faculty related to thequalified projects to be returned in the query results. Such queries may resemble structured databasequeries except that the data to be dealt with are Web pages. A more expressive query model can support
very precise information search and reduce the amount of irrelevant Web information in the results [15].

•Better browsing capabilities: Similar to searching, Web pages can be browsed based on their ontology
concepts and relationships instead of following Web links only. If Web pages are the concept instances,
relationship instances can be created as some virtual links between Web pages. Other than selecting
Web pages belonging to concepts of interest, one can thus navigate the virtual links between Web pages
enriching the browsing experience in digital library applications[15]. On the other hand, if some text
elements are identified as concept instances and their relationships are extracted, they can also bemarked up in Web pages to direct user attentions to the more important text passages.

•

Personalization of Web data access: Personalization aims to find a subset of Web data that matches theinterest profile of a user or a group of users. This can be achieved by recommending Web pages or
Websites to the user(s), or by filtering Web pages that are of interest to the user(s). For example, this candone by analysing the historical data recording user accesses to Web data, and mining the topics relevant
to a user by clustering previously accessed Web pages based on content similarities. When a new Web
page is found to be similar to one of the clusters, it can be routed to the user. As Web pages are annotatedwith ontology entity labels, the grouping of Web pages accessed by a user can be more effectively done
leading to more effective content recommendation.

HOMEPAGE MINING AND HOMEPAGE RELATIONSHIP MINING
Homepage mining and homepage relationship mining are two related kinds of ontology-based Web content
mining where an input ontology is provided as the domain specific knowledge structure [9,10]. In homepage
mining, one aims to find homepages representing concept instances. As the name suggests, homepage
relationship mining refers the discovery of homepage pairs as related concept instances.
Web Unit-Based Homepage Mining
In homepage mining, it is assumed that some Web pages are designated as homepages of concept instances
and they provide the links to other Web pages that supplement the content in homepages. The latter is known
as the support pages. Consider the Website of some university, there are usually homepages created foruniversity departments, faculty members, students, courses, and other concepts in the University ontology
example. These homepages are also often the targets of Web queries and therefore important to mine. An
obvious approach to solve homepage mining problem is to model it as a Web page classification problem.

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Nevertheless, Web page classification mainly assigns one or more concept labels to every Web page based on
its own content. It does not really consider the context of the Web page consisting of other neigboring Web
pages so as to determine if it is a homepage.
In homepage mining, due to the role of homepages and their support pages, we can construct for eachhomepage a Web unit to represent the complete information of the corresponding concept instance. A Web
unit consists of exactly one homepage (also called a key page in [9]) and zero or more support pages. With
this definition, homepage mining can be formulated as a problem of finding Web units representing concept
instances. Once Web units are found, so are the corresponding homepages. We therefore call this Web
unit-based homepage mining.
Figure 2 depicts the Web unit of the G-Portal research project. It consists of a homepage linking to several
support pages.
Support Pages
People Web Page
Homepage
Sub-Project Web
Page

Research Web
Page

Publication Web
Page

News Web Page

Fig. 2 A Web Unit Example
Web unit-based homepage mining has two main tasks: finding the set of Web pages forming each Web unit,
and assigning the Web unit a concept label. The two tasks are complicated by the following two factors:• The notion of Web unit is subjective as the determination of homepage and support pages of a Web unit
may be different for different users.
• Support pages may not always be linked (directly or indirectly) from homepages. For example, someWeb pages containing homework assignments may not be linked from a course’s homepage and this has
some implications in the design of Web unit-based homepage mining methods.
In [9], we proposed an iterative Web unit mining algorithm (iWUM) to constuct and classify Web units.
Once the Web units are mined, the homepages will also be discovered. The iWUM algorithm is designed
based on the following observations:• Web pages from the same file directories also known as Web folders are more semantically related thanWeb pages from different Web folders.
• Support pages of a Web unit are usually reachable from the homepage through some intra-unit links.
• The homepage of a Web unit is usually found at the highest level Web folder containing the pages of the
Web unit.
• Two Web units corresponding to instances of the same concept with no recursive relationship seldom
have direct links between them.

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•
•

Multi-page Web units of the same concept often reside in a set of folders, one for each Web unit and thefolders are directly under a common parent folder.
The homepages of Web units of the same concept are often the link targets of a hub page which may befound at: (a) the folder where the Web units are located if the latter are one-page Web units; (b) the
parent (or ancestor) folder of the folder(s) where the Web units are located if the latter are multi-paged.

As depicted in Figure 3, the steps of iWUM can be divided into two main phases, namely the Web fragment
generation phase and the Web unit merging phase. Web fragments are Web units or parts of Web units. LikeWeb unit, a Web fragment consists of a key page and zero or more support pages. However, the key page is
not necessary a homepage. iWUM essentially constructs Web fragments and assigns them concept labels,
and iteratively combines labelled Web fragments into larger Web units until all the Web units are formed.

Web pagesof a Website

Phase 1:Web fragment
generation

Web Directory
Construction

Web Unit
Construction

Web Fragment
Generation

Web Unit
Classifier Learning

Web Fragment
Classification

Web Unit
Classification

Phase 2:Web fragment
mergingWeb
units

Fig. 3 Iterative Web Unit Mining (iWUM) Method
In Web directory construction, iWUM builds a tree of folders and Web pages from the URLs of Web pages.
The generation of Web fragments is done by computing the connectivity index of each Web folder so as toindicate the extent to which the Web pages and sub-folders directly under the folder are connected. The
more connected they are, the higher is the connectivity index. Starting from the Web folder with the least
connectivity index, iWUM constructs Web fragments with some heuristics to determining key and support
pages. These Web fragments are then classified with concept labels. Here, the classifier of each concept canbe trained using Web units from a Website designated for training. The Web fragments with and without
concept label assignments are then used to construct Web units in the Web unit construction step. This step
involves merging an unlabelled Web fragment with a labelled Web fragment to form a larger Web unit. Web
unit classification further uses the content features and Website structure features to reassign concept labels
to all Web units. With new concept label assignments, larger Web units are constructed again and theprocess repeats until the changes to Web unit labels are minimal.
iWUM has been applied to the well known WebKB dataset in a series of experiments. WebKB consists ofmore than 4000 Web pages from 4 university Websites and it adopts a University ontology similar to that in
Figure 1. It was shown that Web units can be effectively determined using the algorithms and be assigned thecorrect concept labels. In other words, the homepage mining objective is achieved. The experimental results
have also shown that iWUM worked extremely well for fairly well structured Websites.

6Web Unit-Based Homepage Relationship Mining
Homepage relationship mining assumes that homepages of concept instances are known, possibly using
Web-unit based homepage mining method. It can be formulated as a binary classification. That is, given a
relationship rk (c s , ct ) where cs and ct are the source and target concepts of the relationship rk respectively,
one has to determine whether a pair of homepages h s , h t is an instance of the relationship rk (c s , ct )where hs ∈ cs and ht ∈ ct . The general solution to homepage relationship mining consists of three steps,
namely candidate homepage pair generation, feature extraction, and classifier learning.
Homepage relationship mining is special in two aspects. For a given relationship rk (cs , ct ) , one has togenerate candidates of hompage pairs by considering pairs of homepages belonging to cs and ct respectively.
A naïve approach to get all possible pairs however can lead to a very large number. For example, for m andn homepages of cs and ct respectively, there can be mn candidate pairs causing much classification overheads.
Moreover, the features for representing homepage pairs have to be carefully determined since a poor choiceof feature set can lead to poor classification accuracies. In our earlier research, we addressed the above issues
by introducing inter-homepage features and zero-filter [10].
Although a Web page can be represented by a set of features derived from its content, anchor text ofincoming links, and HTML tags, a straightforward composition of these features for a pair of homepages
does not necessarily work well in homepage relationship mining. Our experiments have shown that simple
feature composition yield very poor mining accuracies. We therefore adopt some specially definedinter-homepage features to characterize the background relations between a pair of homepages h s , h t .
These features include:•

Navigation features: The series of links between homepages may suggest the relationship between
them. Since we have the notion of Web units, a link can be either an intra-unit or inter-unit link.
Inter-unit links usually capture more information about the relationships and we therefore derive thenavigation features from these inter-unit links only. There are altogether 24 different navigation featuresthat can be derived from direct links and indirect links with one intermediate Web page. For example,−s−h− p −h→ ht , and hs h
hs h→
ht , hs h→ ht represent a direct link from hs to ht, a direct link from hs
to the support page of Web unit with homepage ht, and a indirect link from hs to ht. The full set ofnavigation features are therefore:−hs−sh− p−hh− p− ss − p −hs− p−ss−h
hs h→
ht , hs → ht , hs → ht , hs → ht , hs → ht , hs → ht , hs → ht ,s−s
h− p −hh− p − ss − p−hs− p−s−hs −hht h→hs , ht → hs , ht → hs , ht → hs , ht → hs , ht → hs , ht → hs ,h− p−hh− p −ss − p −hs− p−shs ←→ ht , hs ←→ ht , hs ←→ ht , hs ←→ ht ,
h− p −hh− p − ss− p −hs− p − shs ←→ht , hs ←→ht , hs ←→ht , hs ←→ht

h− p −hwhere ←→ represents two outgoing links from an intermediate Web page to the two homepages,
h− p−hand ← → represents two outgoing links from the two homepages to an intermediate Web page.
•••

Relative-location features: Relative-location features are association between two homepages’
locations in the Web directory. They include parent-child, sibling and ancestor-descendent.
Common-item features: Common-item features refer to comon items shared by the pair of homepages.
Examples are email addresses, people names, and phone numbers. In our experiments, we have usedemail addresses as common items.
Supplementary features: These are additional features derived for some inter-homepage features andmust be used together with their associated inter-homepage features. In our experiments, we have usedanchor terms associated with direct links between homepages.

Having defined the above inter-homepage features, we use them to represent each homepage pair for
relationship mining. A zero-filter is adopted to remove those homepage pairs that have homepages are notrelated to each other by any of the inter-homepage features.

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We conducted experiments on the classification method for homepage relationship mining on the WebKB
dataset. There were three relationships involved in the experiments, namely Department_Of(Person,
Department), Member_Of(Person, Project), and Instructor_Of(Course, Person). The experiments used
three universities’ Web pages for training and the remaining ones for testing. SVM classifiers were learnt
using the training set. It was found that good classification accuracy was achieved using navigation features
alone, while other inter-homepage features helped to improve accuracies in lesser extent. It was also foundthat the results of homepage relationship mining were very dependent on the quality of homepage mining. If
homepages could not be determined accurately, the errors would propagate further to homepage relationshipmining.

SUMMARY AND CONCLUSIONS
Ontology is a some domain knowledge that could be used to describe information on the Web. This paper
summarizes the use of ontology in Web mining. In particular, we focus on how ontology has been
incorporated in Web mining. We also use two special Web content mining problems known as homepage
mining and homepage relationship mining as examples, and present our solutions. As more ontologies getdeveloped covering a wide of domains and the need for advanced Web search increases, more
ontology-based Web mining research will be required. At the same time, we also envisage many of thesesolution techniques will be implemented in the emerging digital library applications.

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